Demonstrating Heterogeneity of Treatment Effects Among Patients: An Overlooked but Important Step Toward Precision Medicine

Potential interplay between tumor size and vitamin D receptor (VDR) polymorphisms in breast cancer prognosis: a prospective cohort study

PURPOSE

Vitamin D has some anticancer properties that may decrease breast cancer risk and improve prognosis. The aim was to investigate associations between four previously studied VDR SNPs (Taq1, Tru91, Bsm1, and Fok1) and prognosis in different groups of breast cancer patients.

METHODS

VDR genotyping of 1,017 breast cancer patients included 2002-2012 in Lund, Sweden, was performed using Oncoarray. Follow-up was until June 30, 2019. Clinical data and patient information were collected from medical records and questionnaires. Cox regression was used for survival analyses.

RESULTS

Genotype frequencies were as follows: Fok1 (AA 15.7%, AG 49.1%, GG 35.1%), Bsm1 (CC 37.2%, CT 46.1%, TT 16.7%), Tru91 (CC 77.8%, CT 20.7%, TT 1.5%), and Taq1 (AA 37.2%, AG 46.2%, GG 16.6%). During follow-up there were 195 breast cancer events. The homozygous variants of Taq1 and Bsm1 were associated with reduced risk of breast cancer events (adjusted HR = 0.59, 95% CI 0.38-0.92 for Taq1 and adjusted HR = 0.61, 95% CI 0.40-0.94 for Bsm1). The G allele of the Fok1 was associated with increased risk of breast cancer events in small tumors (pT1, adjusted HR = 1.83, 95% CI 1.04-3.23) but not in large tumors (pT2/3/4, adjusted HR = 0.80, 95% CI 0.41-1.59) with a borderline interaction (Pinteraction = 0.058). No interactions between VDR genotypes and adjuvant treatments regarding breast cancer prognosis were detected.

CONCLUSION

VDR genotypes were associated with breast cancer prognosis and the association might be modified by tumor size. Further research is needed to confirm the findings and elucidate their potential clinical implications.

Density physics-informed neural networks reveal sources of cell heterogeneity in signal transduction

The transduction time between signal initiation and final response provides valuable information on the underlying signaling pathway, including its speed and precision. Furthermore, multi-modality in a transduction-time distribution indicates that the response is regulated by multiple pathways with different transduction speeds. Here, we developed a method called density physics-informed neural networks (Density-PINNs) to infer the transduction-time distribution from measurable final stress response time traces. We applied Density-PINNs to single-cell gene expression data from sixteen promoters regulated by unknown pathways in response to antibiotic stresses. We found that promoters with slower signaling initiation and transduction exhibit larger cell-to-cell heterogeneity in response intensity. However, this heterogeneity was greatly reduced when the response was regulated by slow and fast pathways together. This suggests a strategy for identifying effective signaling pathways for consistent cellular responses to disease treatments. Density-PINNs can also be applied to understand other time delay systems, including infectious diseases.

SOMAScience: A Novel Platform for Multidimensional, Longitudinal Pain Assessment

Chronic pain is one of the most significant health issues in the United States, affecting more than 20% of the population. Despite its contribution to the increasing health crisis, reliable predictors of disease development, progression, or treatment outcomes are lacking. Self-report remains the most effective way to assess pain, but measures are often acquired in sparse settings over short time windows, limiting their predictive ability. In this paper, we present a new mobile health platform called SOMAScience. SOMAScience serves as an easy-to-use research tool for scientists and clinicians, enabling the collection of large-scale pain datasets in single- and multicenter studies by facilitating the acquisition, transfer, and analysis of longitudinal, multidimensional, self-report pain data. Data acquisition for SOMAScience is done through a user-friendly smartphone app, SOMA, that uses experience sampling methodology to capture momentary and daily assessments of pain intensity, unpleasantness, interference, location, mood, activities, and predictions about the next day that provide personal insights into daily pain dynamics. The visualization of data and its trends over time is meant to empower individual users' self-management of their pain. This paper outlines the scientific, clinical, technological, and user considerations involved in the development of SOMAScience and how it can be used in clinical studies or for pain self-management purposes. Our goal is for SOMAScience to provide a much-needed platform for individual users to gain insight into the multidimensional features of their pain while lowering the barrier for researchers and clinicians to obtain the type of pain data that will ultimately lead to improved prevention, diagnosis, and treatment of chronic pain.

Comparing algorithms for characterizing treatment effect heterogeneity in randomized trials

The identification and estimation of heterogeneous treatment effects in biomedical clinical trials are challenging, because trials are typically planned to assess the treatment effect in the overall trial population. Nevertheless, the identification of how the treatment effect may vary across subgroups is of major importance for drug development. In this work, we review some existing simulation work and perform a simulation study to evaluate recent methods for identifying and estimating the heterogeneous treatments effects using various metrics and scenarios relevant for drug development. Our focus is not only on a comparison of the methods in general, but on how well these methods perform in simulation scenarios that reflect real clinical trials. We provide the R package benchtm that can be used to simulate synthetic biomarker distributions based on real clinical trial data and to create interpretable scenarios to benchmark methods for identification and estimation of treatment effect heterogeneity.

Are There Interindividual Responses of Cardiovascular Disease Risk Markers to Acute Exercise? A Replicate Crossover Trial

PURPOSE

Using a replicated crossover design, we quantified the response heterogeneity of postprandial cardiovascular disease risk marker responses to acute exercise.

METHODS

Twenty men (mean (SD) age, 26 (6) yr; body mass index, 23.9 (2.4) kg·m -2 ) completed four 2-d conditions (two control, two exercise) in randomized orders. On days 1 and 2, participants rested and consumed two high-fat meals over 9 h. Participants ran for 60 min (61 (7)% of peak oxygen uptake) on day 1 (6.5 to 7.5 h) of both exercise conditions. Time-averaged total area under the curve (TAUC) for triacylglycerol, glucose, and insulin were calculated from 11 venous blood samples on day 2. Arterial stiffness and blood pressure responses were calculated from measurements at baseline on day 1 and at 2.5 h on day 2. Consistency of individual differences was explored by correlating the two replicates of control-adjusted exercise responses for each outcome. Within-participant covariate-adjusted linear mixed models quantified participant-by-condition interactions and individual response SDs.

RESULTS

Acute exercise reduced mean TAUC-triacylglycerol (-0.27 mmol·L -1 ·h; Cohen's d = 0.29, P = 0.017) and TAUC-insulin (-25 pmol·L -1 ·h; Cohen's d = 0.35, P = 0.022) versus control, but led to negligible changes in TAUC-glucose and the vascular outcomes (Cohen's d ≤ 0.36, P ≥ 0.106). Small-to-moderate, but nonsignificant, correlations were observed between the two response replicates ( r = -0.42 to 0.15, P ≥ 0.066). We did not detect any individual response heterogeneity. All participant-by-condition interactions were P ≥ 0.137, and all individual response SDs were small with wide 95% confidence intervals overlapping zero.

CONCLUSIONS

Large trial-to-trial within-subject variability inhibited detection of consistent interindividual variability in postprandial metabolic and vascular responses to acute exercise.

Design, analysis, and interpretation of treatment response heterogeneity in personalized nutrition and obesity treatment research

It is increasingly assumed that there is no one-size-fits-all approach to dietary recommendations for the management and treatment of chronic diseases such as obesity. This phenomenon that not all individuals respond uniformly to a given treatment has become an area of research interest given the rise of personalized and precision medicine. To conduct, interpret, and disseminate this research rigorously and with scientific accuracy, however, requires an understanding of treatment response heterogeneity. Here, we define treatment response heterogeneity as it relates to clinical trials, provide statistical guidance for measuring treatment response heterogeneity, and highlight study designs that can quantify treatment response heterogeneity in nutrition and obesity research. Our goal is to educate nutrition and obesity researchers in how to correctly identify and consider treatment response heterogeneity when analyzing data and interpreting results, leading to rigorous and accurate advancements in the field of personalized medicine.

Extending the 'host response' paradigm from sepsis to cardiogenic shock: evidence, limitations and opportunities

Recent clinical and research efforts in cardiogenic shock (CS) have largely focussed on the restoration of the low cardiac output state that is the conditio sine qua non of the clinical syndrome. This approach has failed to translate into improved outcomes, and mortality has remained static at 30-50%. There is an unmet need to better delineate the pathobiology of CS to understand the observed heterogeneity of presentation and treatment effect and to identify novel therapeutic targets. Despite data in other critical illness syndromes, specifically sepsis, the role of dysregulated inflammation and immunity is hitherto poorly described in CS. High-dimensional molecular profiling, particularly through leukocyte transcriptomics, may afford opportunity to better characterise subgroups of patients with shared mechanisms of immune dysregulation. In this state-of-the-art review, we outline the rationale for considering molecular subtypes of CS. We describe how high-dimensional molecular technologies can be used to identify these subtypes, and whether they share biological features with sepsis and other critical illness states. Finally, we propose how the identification of molecular subtypes of patients may enrich future clinical trial design and identification of novel therapies for CS.

Causal models and causal modelling in obesity: foundations, methods and evidence

Discussing causes in science, if we are to do so in a way that is sensible, begins at the root. All too often, we jump to discussing specific postulated causes but do not first consider what we mean by, for example, causes of obesity or how we discern whether something is a cause. In this paper, we address what we mean by a cause, discuss what might and might not constitute a reasonable causal model in the abstract, speculate about what the causal structure of obesity might be like overall and the types of things we should be looking for, and finally, delve into methods for evaluating postulated causes and estimating causal effects. We offer the view that different meanings of the concept of causal factors in obesity research are regularly being conflated, leading to confusion, unclear thinking and sometimes nonsense. We emphasize the idea of different kinds of studies for evaluating various aspects of causal effects and discuss experimental methods, assumptions and evaluations. We use analogies from other areas of research to express the plausibility that only inelegant solutions will be truly informative. Finally, we offer comments on some specific postulated causal factors. This article is part of a discussion meeting issue 'Causes of obesity: theories, conjectures and evidence (Part II)'.

Critical Insights Into the Management of Postpartum Left Main Spontaneous Coronary Artery Dissection: Current Strategies and Future Directions

This review article delves into the multifaceted realm of postpartum left main spontaneous coronary artery dissection (PLMSCAD), an infrequent yet critical condition affecting women during the postpartum period. Through a comprehensive exploration of its pathophysiology, clinical presentation, diagnosis, management strategies, and future directions, this review provides a holistic understanding of PLMSCAD's complexities. The article highlights challenges in diagnosis due to overlapping symptoms and underscores the significance of prompt recognition and tailored interventions. Current management strategies, encompassing medical and interventional approaches, are analysed in the context of their short-term and long-term impact on patient outcomes. Ethical considerations and the role of patient education and support networks are explored, shedding light on the broader psychosocial dimensions of PLMSCAD management. As emerging research reveals insights into genetic influences, hormonal dynamics, and the prognosis of affected individuals, this review emphasises the necessity of collaborative research endeavours and data sharing to enhance our understanding and guide future strategies. Ultimately, this review underscores the urgency of addressing the unique needs of women experiencing PLMSCAD, urging ongoing research, multidisciplinary collaboration, and a patient-centred approach to optimise maternal health outcomes and well-being.

Optimizing and Accelerating the Development of Precision Pain Treatments for Chronic Pain: IMMPACT Review and Recommendations

Large variability in the individual response to even the most-efficacious pain treatments is observed clinically, which has led to calls for a more personalized, tailored approach to treating patients with pain (ie, "precision pain medicine"). Precision pain medicine, currently an aspirational goal, would consist of empirically based algorithms that determine the optimal treatments, or treatment combinations, for specific patients (ie, targeting the right treatment, in the right dose, to the right patient, at the right time). Answering this question of "what works for whom" will certainly improve the clinical care of patients with pain. It may also support the success of novel drug development in pain, making it easier to identify novel treatments that work for certain patients and more accurately identify the magnitude of the treatment effect for those subgroups. Significant preliminary work has been done in this area, and analgesic trials are beginning to utilize precision pain medicine approaches such as stratified allocation on the basis of prespecified patient phenotypes using assessment methodologies such as quantitative sensory testing. Current major challenges within the field include: 1) identifying optimal measurement approaches to assessing patient characteristics that are most robustly and consistently predictive of inter-patient variation in specific analgesic treatment outcomes, 2) designing clinical trials that can identify treatment-by-phenotype interactions, and 3) selecting the most promising therapeutics to be tested in this way. This review surveys the current state of precision pain medicine, with a focus on drug treatments (which have been most-studied in a precision pain medicine context). It further presents a set of evidence-based recommendations for accelerating the application of precision pain methods in chronic pain research. PERSPECTIVE: Given the considerable variability in treatment outcomes for chronic pain, progress in precision pain treatment is critical for the field. An array of phenotypes and mechanisms contribute to chronic pain; this review summarizes current knowledge regarding which treatments are most effective for patients with specific biopsychosocial characteristics.

“Adipose-derived mesenchymal stem cell therapy for the management of female sexual dysfunction: Literature reviews and study design of a clinical trial”

Hormone imbalance and female sexual dysfunction immensely affect perimenopausal female health and quality of life. Hormone therapy can improve female hormone deficiency, but long-term use increases the risk of cardiovascular diseases and cancer. Therefore, it is necessary to develop a novel effective treatment to achieve long-term improvement in female general and sexual health. This study reviewed factors affecting syndromes of female sexual dysfunction and its current therapy options. Next, the authors introduced research data on mesenchymal stromal cell/mesenchymal stem cell (MSC) therapy to treat female reproductive diseases, including Asherman’s syndrome, premature ovarian failure/primary ovarian insufficiency, and vaginal atrophy. Among adult tissue-derived MSCs, adipose tissue-derived stem cells (ASCs) have emerged as the most potent therapeutic cell therapy due to their abundant presence in the stromal vascular fraction of fat, high proliferation capacity, superior immunomodulation, and strong secretion profile of regenerative factors. Potential mechanisms and side effects of ASCs for the treatment of female sexual dysfunction will be discussed. Our phase I clinical trial has demonstrated the safety of autologous ASC therapy for women and men with sexual hormone deficiency. We designed the first randomized controlled crossover phase II trial to investigate the safety and efficacy of autologous ASCs to treat female sexual dysfunction in perimenopausal women. Here, we introduce the rationale, trial design, and methodology of this clinical study. Because aging and metabolic diseases negatively impact the bioactivity of adult-derived MSCs, this study will use ASCs cultured in physiological oxygen tension (5%) to cope with these challenges. A total of 130 perimenopausal women with sexual dysfunction will receive two intravenous infusions of autologous ASCs in a crossover design. The aims of the proposed study are to evaluate 1) the safety of cell infusion based on the frequency and severity of adverse events/serious adverse events during infusion and follow-up and 2) improvements in female sexual function assessed by the Female Sexual Function Index (FSFI), the Utian Quality of Life Scale (UQOL), and the levels of follicle-stimulating hormone (FSH) and estradiol. In addition, cellular aging biomarkers, including plasminogen activator inhibitor-1 (PAI-1), p16 and p21 expression in T cells and the inflammatory cytokine profile, will also be characterized. Overall, this study will provide essential insights into the effects and potential mechanisms of ASC therapy for perimenopausal women with sexual dysfunction. It also suggests direction and design strategies for future research.

Predicting Sensitivity to Adverse Lifestyle Risk Factors for Cardiometabolic Morbidity and Mortality

People appear to vary in their susceptibility to lifestyle risk factors for cardiometabolic disease; determining a priori who is most sensitive may help optimize the timing, design, and delivery of preventative interventions. We aimed to ascertain a person’s degree of resilience or sensitivity to adverse lifestyle exposures and determine whether these classifications help predict cardiometabolic disease later in life; we pooled data from two population-based Swedish prospective cohort studies (n = 53,507), and we contrasted an individual’s cardiometabolic biomarker profile with the profile predicted for them given their lifestyle exposure characteristics using a quantile random forest approach. People who were classed as ‘sensitive’ to hypertension- and dyslipidemia-related lifestyle exposures were at higher risk of developing cardiovascular disease (CVD, hazards ratio 1.6 (95% CI: 1.3, 1.91)), compared with the general population. No differences were observed for type 2 diabetes (T2D) risk. Here, we report a novel approach to identify individuals who are especially sensitive to adverse lifestyle exposures and who are at higher risk of subsequent cardiovascular events. Early preventive interventions may be needed in this subgroup.

Measuring Individual Benefits of Medical Treatments Using Longitudinal Hospital Data with Non-Ignorable Missing Responses Caused by Patient Discharge: Application to the Study of Benefits of Pain Management Post Spinal Fusion

Electronic health records (EHR) provide valuable resources for longitudinal studies and understanding risk factors associated with poor clinical outcomes. However, they may not contain complete follow-ups, and the missing data may not be at random since hospital discharge may depend in part on expected but unrecorded clinical outcomes that occur after patient discharge. These non-ignorable missing data requires appropriate analysis methods. Here, we are interested in measuring and analyzing individual treatment benefits of medical treatments in patients recorded in EHR databases. We present a method for predicting individual benefits that handles non-ignorable missingness due to hospital discharge. The longitudinal clinical outcome of interest is modeled simultaneously with the hospital length of stay using a joint mixed-effects model, and individual benefits are predicted through a frequentist approach: the empirical Bayesian approach. We illustrate our approach by assessing individual pain management benefits to patients who underwent spinal fusion surgery. By calculating sample percentiles of empirical Bayes predictors of individual benefits, we examine the evolution of individual benefits over time. We additionally compare these percentiles with percentiles calculated with a Monte Carlo approach. We showed that empirical Bayes predictors of individual benefits do not only allow examining benefits in specific patients but also reflect overall population trends reliably.

Systematic inference identifies a major source of heterogeneity in cell signaling dynamics: The rate-limiting step number

Identifying the sources of cell-to-cell variability in signaling dynamics is essential to understand drug response variability and develop effective therapeutics. However, it is challenging because not all signaling intermediate reactions can be experimentally measured simultaneously. This can be overcome by replacing them with a single random time delay, but the resulting process is non-Markovian, making it difficult to infer cell-to-cell heterogeneity in reaction rates and time delays. To address this, we developed an efficient and scalable moment-based Bayesian inference method (MBI) with a user-friendly computational package that infers cell-to-cell heterogeneity in the non-Markovian signaling process. We applied MBI to single-cell expression profiles from promoters responding to antibiotics and discovered a major source of cell-to-cell variability in antibiotic stress response: the number of rate-limiting steps in signaling cascades. This knowledge can help identify effective therapies that destroy all pathogenic or cancer cells, and the approach can be applied to precision medicine.

Using population crossover trials to improve the decision process regarding treatment individualization in N-of-1 trials

Healthcare researchers are showing renewed interest in the utilization of N-of-1 clinical trials for the individualization of pharmacological treatments. Here, we propose a frequentist approach to conducting treatment individualization in N-of-1 trials that we call "partial empirical Bayes." We infer the most beneficial treatment for the patient from combining the information provided by a previously conducted population crossover trial with individual patient data. We propose a method for estimating an optimal number of treatment cycles and investigate the statistical conditions under which N-of-1 trials are more beneficial than traditional clinical approaches. We represent the patient population with a random-coefficients linear model and calculate estimators of posttreatment individual disease severities. We show the estimators' consistency under the most common N-of-1 designs and examine their prediction errors and performance with small numbers of patient's responses. We demonstrate by simulating new patients that our approach is equivalent or superior to both the common clinical practice of recommending the on-average best treatment for all patients and the common individualization method that simply compares average responses to the tested treatments. We conclude that some situations exist in which individualization with N-of-1 trials is highly beneficial while other situations exist in which individualization may be unfruitful.

The ACTTION Guide to Clinical Trials of Pain Treatments, part II: mitigating bias, maximizing value

Summaries of the articles included in part II of the ACTTION Guide to Clinical Trials of Pain Treatments are followed by brief overviews of methodologic considerations involving precision pain medicine, pragmatic clinical trials, real world evidence, and patient engagement in clinical trials.